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<li class="navelem"><a class="el" href="namespacenabu.html">nabu</a></li><li class="navelem"><b>neuralnetworks</b></li><li class="navelem"><b>components</b></li><li class="navelem"><b>beam_search_decoder</b></li><li class="navelem"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html">BeamSearchDecoder</a></li>  </ul>
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<a href="#pub-methods">Public Member Functions</a> &#124;
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<div class="title">nabu.neuralnetworks.components.beam_search_decoder.BeamSearchDecoder Class Reference</div>  </div>
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<p>the beam search decode  
 <a href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#details">More...</a></p>
<div class="dynheader">
Inheritance diagram for nabu.neuralnetworks.components.beam_search_decoder.BeamSearchDecoder:</div>
<div class="dyncontent">
 <div class="center">
  <img src="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.png" usemap="#nabu.neuralnetworks.components.beam_5Fsearch_5Fdecoder.BeamSearchDecoder_map" alt=""/>
  <map id="nabu.neuralnetworks.components.beam_5Fsearch_5Fdecoder.BeamSearchDecoder_map" name="nabu.neuralnetworks.components.beam_search_decoder.BeamSearchDecoder_map">
</map>
 </div></div>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:ae57e76ea61bd677be46903445a628905"><td class="memItemLeft" align="right" valign="top"><a id="ae57e76ea61bd677be46903445a628905"></a>
def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#ae57e76ea61bd677be46903445a628905">batch_size</a> (self)</td></tr>
<tr class="memdesc:ae57e76ea61bd677be46903445a628905"><td class="mdescLeft">&#160;</td><td class="mdescRight">the batch size <br /></td></tr>
<tr class="separator:ae57e76ea61bd677be46903445a628905"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad6d49326cd4c30bdc6c54bd1834fafd4"><td class="memItemLeft" align="right" valign="top"><a id="ad6d49326cd4c30bdc6c54bd1834fafd4"></a>
def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#ad6d49326cd4c30bdc6c54bd1834fafd4">output_size</a> (self)</td></tr>
<tr class="memdesc:ad6d49326cd4c30bdc6c54bd1834fafd4"><td class="mdescLeft">&#160;</td><td class="mdescRight">the output size (empty) <br /></td></tr>
<tr class="separator:ad6d49326cd4c30bdc6c54bd1834fafd4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7984363e9c70931f5ac6e10163867998"><td class="memItemLeft" align="right" valign="top"><a id="a7984363e9c70931f5ac6e10163867998"></a>
def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#a7984363e9c70931f5ac6e10163867998">output_dtype</a> (self)</td></tr>
<tr class="memdesc:a7984363e9c70931f5ac6e10163867998"><td class="mdescLeft">&#160;</td><td class="mdescRight">the output dtype (empty) <br /></td></tr>
<tr class="separator:a7984363e9c70931f5ac6e10163867998"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2ec64aaf4ce60abffebccad0627454f5"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#a2ec64aaf4ce60abffebccad0627454f5">__init__</a> (self, cell, embedding, start_tokens, end_token, initial_state, beam_width, output_layer=None, length_penalty_weight=0.0, temperature=1.0)</td></tr>
<tr class="memdesc:a2ec64aaf4ce60abffebccad0627454f5"><td class="mdescLeft">&#160;</td><td class="mdescRight">constructor  <a href="#a2ec64aaf4ce60abffebccad0627454f5">More...</a><br /></td></tr>
<tr class="separator:a2ec64aaf4ce60abffebccad0627454f5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a418ec10b8af94e9930fb5c3fd5c77c6e"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#a418ec10b8af94e9930fb5c3fd5c77c6e">initialize</a> (self, name=None)</td></tr>
<tr class="memdesc:a418ec10b8af94e9930fb5c3fd5c77c6e"><td class="mdescLeft">&#160;</td><td class="mdescRight">Called before any decoding iterations.  <a href="#a418ec10b8af94e9930fb5c3fd5c77c6e">More...</a><br /></td></tr>
<tr class="separator:a418ec10b8af94e9930fb5c3fd5c77c6e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab240d7cd90c7e0d8ba1fdc177bd19afb"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#ab240d7cd90c7e0d8ba1fdc177bd19afb">step</a> (self, time, inputs, state, name=None)</td></tr>
<tr class="memdesc:ab240d7cd90c7e0d8ba1fdc177bd19afb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Called per step of decoding (but only once for dynamic decoding).  <a href="#ab240d7cd90c7e0d8ba1fdc177bd19afb">More...</a><br /></td></tr>
<tr class="separator:ab240d7cd90c7e0d8ba1fdc177bd19afb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afd54a9fd8dec49fa23f7d695053b6f26"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoder.html#afd54a9fd8dec49fa23f7d695053b6f26">finalize</a> (self, outputs, final_state, sequence_lengths)</td></tr>
<tr class="memdesc:afd54a9fd8dec49fa23f7d695053b6f26"><td class="mdescLeft">&#160;</td><td class="mdescRight">Finalize and return the predicted_ids.  <a href="#afd54a9fd8dec49fa23f7d695053b6f26">More...</a><br /></td></tr>
<tr class="separator:afd54a9fd8dec49fa23f7d695053b6f26"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:a74b3615c2300eae04468d2860d5f8cad"><td class="memItemLeft" align="right" valign="top"><a id="a74b3615c2300eae04468d2860d5f8cad"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>cell</b></td></tr>
<tr class="separator:a74b3615c2300eae04468d2860d5f8cad"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7153408bd7a8c624acf5e87df20d7d91"><td class="memItemLeft" align="right" valign="top"><a id="a7153408bd7a8c624acf5e87df20d7d91"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>embedding</b></td></tr>
<tr class="separator:a7153408bd7a8c624acf5e87df20d7d91"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac1a8474e5f3251a6021b504e168217f9"><td class="memItemLeft" align="right" valign="top"><a id="ac1a8474e5f3251a6021b504e168217f9"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>start_tokens</b></td></tr>
<tr class="separator:ac1a8474e5f3251a6021b504e168217f9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae3e4cd520f03b5f0faa576734c9a9514"><td class="memItemLeft" align="right" valign="top"><a id="ae3e4cd520f03b5f0faa576734c9a9514"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>end_token</b></td></tr>
<tr class="separator:ae3e4cd520f03b5f0faa576734c9a9514"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a483325181cb27a6b8df746d84b78b88c"><td class="memItemLeft" align="right" valign="top"><a id="a483325181cb27a6b8df746d84b78b88c"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>initial_state</b></td></tr>
<tr class="separator:a483325181cb27a6b8df746d84b78b88c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae177b0ffc7c4c46e53bd493d01892316"><td class="memItemLeft" align="right" valign="top"><a id="ae177b0ffc7c4c46e53bd493d01892316"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>beam_width</b></td></tr>
<tr class="separator:ae177b0ffc7c4c46e53bd493d01892316"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a100b1a210e574236dd85a5564da726e7"><td class="memItemLeft" align="right" valign="top"><a id="a100b1a210e574236dd85a5564da726e7"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>output_layer</b></td></tr>
<tr class="separator:a100b1a210e574236dd85a5564da726e7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4c3929fbc8d34cab3050b033d5318081"><td class="memItemLeft" align="right" valign="top"><a id="a4c3929fbc8d34cab3050b033d5318081"></a>
&#160;</td><td class="memItemRight" valign="bottom"><b>length_penalty_weight</b></td></tr>
<tr class="separator:a4c3929fbc8d34cab3050b033d5318081"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>the beam search decode </p>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a id="a2ec64aaf4ce60abffebccad0627454f5"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a2ec64aaf4ce60abffebccad0627454f5">&sect;&nbsp;</a></span>__init__()</h2>

<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">def nabu.neuralnetworks.components.beam_search_decoder.BeamSearchDecoder.__init__ </td>
          <td>(</td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>self</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>cell</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>embedding</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>start_tokens</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>end_token</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>initial_state</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>beam_width</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>output_layer</em> = <code>None</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>length_penalty_weight</em> = <code>0.0</code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>temperature</em> = <code>1.0</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>constructor </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">cell</td><td>An <code>RNNCell</code> instance. </td></tr>
    <tr><td class="paramname">embedding</td><td>A callable that takes a vector tensor of <code>ids</code> (argmax ids), or the <code>params</code> argument for <code>embedding_lookup</code>. </td></tr>
    <tr><td class="paramname">start_tokens</td><td><code>int32</code> vector shaped <code>[batch_size]</code>, the start </td></tr>
    <tr><td class="paramname">tokens.</td><td></td></tr>
    <tr><td class="paramname">end_token</td><td><code>int32</code> scalar, the token that marks end of decoding. </td></tr>
    <tr><td class="paramname">initial_state</td><td>A (possibly nested tuple of...) tensors and </td></tr>
    <tr><td class="paramname">TensorArrays.</td><td></td></tr>
    <tr><td class="paramname">beam_width</td><td>Python integer, the number of beams. </td></tr>
    <tr><td class="paramname">output_layer</td><td>(Optional) An instance of <code>tf.layers.Layer</code>, i.e., <code>tf.layers.Dense</code>. Optional layer to apply to the RNN output prior to storing the result or sampling. </td></tr>
    <tr><td class="paramname">length_penalty_weight</td><td>Float weight to penalize length. Disabled with 0.0. </td></tr>
    <tr><td class="paramname">temperature</td><td>a temperature to apply before the softmax to smooth or sharpen the probabilities. High temperature means a smooth </td></tr>
    <tr><td class="paramname">distribution</td><td></td></tr>
  </table>
  </dd>
</dl>

</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
<a id="afd54a9fd8dec49fa23f7d695053b6f26"></a>
<h2 class="memtitle"><span class="permalink"><a href="#afd54a9fd8dec49fa23f7d695053b6f26">&sect;&nbsp;</a></span>finalize()</h2>

<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">def nabu.neuralnetworks.components.beam_search_decoder.BeamSearchDecoder.finalize </td>
          <td>(</td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>self</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>outputs</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>final_state</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>sequence_lengths</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Finalize and return the predicted_ids. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">outputs</td><td>An instance of <a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoderOutput.html" title="class for the output of the BeamSearchDecoder ">BeamSearchDecoderOutput</a>. </td></tr>
    <tr><td class="paramname">final_state</td><td>An instance of <a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchState.html" title="class for the beam search state ">BeamSearchState</a>. </td></tr>
    <tr><td class="paramname">sequence_lengths</td><td>An <code>int64</code> tensor shaped <code>[batch_size, beam_width]</code>. The sequence lengths </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd><ul>
<li>An instance of <a class="el" href="classnabu_1_1neuralnetworks_1_1components_1_1beam__search__decoder_1_1BeamSearchDecoderFinalOutput.html" title="class for the final output of the BeamSearchDecoder ">BeamSearchDecoderFinalOutput</a></li>
<li>The final state </li>
</ul>
</dd></dl>

</div>
</div>
<a id="a418ec10b8af94e9930fb5c3fd5c77c6e"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a418ec10b8af94e9930fb5c3fd5c77c6e">&sect;&nbsp;</a></span>initialize()</h2>

<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">def nabu.neuralnetworks.components.beam_search_decoder.BeamSearchDecoder.initialize </td>
          <td>(</td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>self</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>name</em> = <code>None</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Called before any decoding iterations. </p>
<p>This methods must compute initial input values and initial state.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">name</td><td>Name scope for any created operations. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd><code>(finished, initial_inputs, initial_state)</code>: initial values of 'finished' flags, inputs and state. </dd></dl>

</div>
</div>
<a id="ab240d7cd90c7e0d8ba1fdc177bd19afb"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ab240d7cd90c7e0d8ba1fdc177bd19afb">&sect;&nbsp;</a></span>step()</h2>

<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">def nabu.neuralnetworks.components.beam_search_decoder.BeamSearchDecoder.step </td>
          <td>(</td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>self</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>time</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>inputs</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>state</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">&#160;</td>
          <td class="paramname"><em>name</em> = <code>None</code>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Called per step of decoding (but only once for dynamic decoding). </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">time</td><td>Scalar <code>int32</code> tensor. Current step number. </td></tr>
    <tr><td class="paramname">inputs</td><td>RNNCell input (possibly nested tuple of) tensor[s] for this time step. </td></tr>
    <tr><td class="paramname">state</td><td>RNNCell state (possibly nested tuple of) tensor[s] from previous time step. </td></tr>
    <tr><td class="paramname">name</td><td>Name scope for any created operations.</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd><code>(outputs, next_state, next_inputs, finished)</code>: <code>outputs</code> is an object containing the decoder output, <code>next_state</code> is a (structure of) state tensors and TensorArrays, <code>next_inputs</code> is the tensor that should be used as input for the next step, <code>finished</code> is a boolean tensor telling whether the sequence is complete, for each sequence in the batch. </dd></dl>

</div>
</div>
<hr/>The documentation for this class was generated from the following file:<ul>
<li>components/beam_search_decoder.py</li>
</ul>
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